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Related Experiment Video

Updated: Jun 23, 2026

Understanding the Changes in Mitochondrial Morphology through Dynamic and Three-dimensional Fluorescence Micrographs

Understanding the Changes in Mitochondrial Morphology through Dynamic and Three-dimensional Fluorescence Micrographs

Published on: August 15, 2025

GPU-accelerated MitoGraph for high-throughput three-dimensional mitochondrial morphology analysis.

Siddharth Nahar^1,2, Zichen Wang^1,2, Eric Arkfeld^1,2

¹Department of Pharmacology, University of California San Diego, Room 106B, La Jolla, San Diego, CA, 92093, United States.

BMC Bioinformatics

|June 20, 2026

Summary

This summary is machine-generated.

MitoGraph-GPU accelerates mitochondrial network segmentation in 4D microscopy data, offering significant speedups for live-cell imaging. This GPU implementation enables efficient analysis of large datasets, crucial for high-throughput mitochondrial phenotyping.

Keywords:

Fluorescence microscopy GPU acceleration Image processing Image segmentation Mitochondria Network analysis

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A Faster, High Resolution, mtPA-GFP-based Mitochondrial Fusion Assay Acquiring Kinetic Data of Multiple Cells in Parallel Using Confocal Microscopy

Published on: July 20, 2012

Area of Science:

Cell Biology
Microscopy Imaging
Computational Biology

Background:

Automated segmentation of mitochondrial networks is vital for understanding cellular function.
Current CPU-based methods are computationally intensive, limiting analysis of large 4D live-cell microscopy datasets.
Advanced microscopes like lattice light-sheet microscopy generate massive datasets requiring faster processing.

Purpose of the Study:

To develop a GPU-accelerated version of MitoGraph for efficient 4D mitochondrial network segmentation.
To address the computational bottleneck of processing large-scale live-cell microscopy data.
To enable high-throughput 4D mitochondrial phenotyping.

Main Methods:

Developed MitoGraph-GPU, a Python-based implementation utilizing CuPy for GPU acceleration.
Optimized Hessian/eigenvalue computations, vesselness filtering, skeletonization, and topology analysis.
Validated performance on budding yeast and human lung organoid datasets from lattice light-sheet microscopy.

Main Results:

Achieved up to 11x speedup in yeast and 30x speedup per frame in lung cells.
Preserved segmentation fidelity with ~99.9% agreement in maximum intensity projections.
Reduced processing time for a large 4D organoid dataset from ~500 hours (CPU) to ~20 hours (GPU), a 25x improvement.

Conclusions:

MitoGraph-GPU significantly enhances the throughput of mitochondrial network segmentation for 4D live-cell microscopy.
The tool enables practical, scalable, high-throughput analysis of large 4D datasets.
Facilitates downstream mitochondrial tracking and phenotyping analyses.